Transcriptional plasticity drives leukemia immune escape

Relapse of acute myeloid leukemia (AML) after allogeneic bone marrow transplantation (alloSCT) has been linked to immune evasion due to reduced expression of major histocompatibility complex class II (MHC-II) proteins through unknown mechanisms. We developed CORENODE, a computational algorithm for genome-wide transcription network decomposition, that identified the transcription factors (TFs) IRF8 and MEF2C as positive regulators and MYB and MEIS1 as negative regulators of MHC-II expression in AML cells. We show that reduced MHC-II expression at relapse is transcriptionally driven by combinatorial changes in the levels of these TFs, acting both independently and through the MHC-II coactivator CIITA. Beyond the MHC-II genes, MYB and IRF8 antagonistically regulate a broad genetic program responsible for cytokine signaling and T-cell stimulation that displays reduced expression at relapse. A small number of cells with altered TF levels and silenced MHC-II expression are present at the time of initial leukemia diagnosis, likely contributing to eventual relapse. Our findings reveal an adaptive transcriptional mechanism of AML evolution after allogenic transplantation whereby combinatorial fluctuations of TF levels under immune pressure result in selection of cells with a silenced T-cell stimulation program.

Measuring the Network Vulnerability Based on Markov Criticality

Vulnerability assessment—a critical issue for networks—attempts to foresee unexpected destructive events or hostile attacks in the whole system. In this article, we consider a new Markov global connectivity metric—Kemeny constant, and take its derivative called Markov criticality to identify critical links. Markov criticality allows us to find links that are most influential on the derivative of Kemeny constant. Thus, we can utilize it to identity a critical link ( i , j ) from node i to node j , such that removing it leads to a minimization of networks’ global connectivity, i.e., the Kemeny constant. Furthermore, we also define a novel vulnerability index to measure the average speed by which we can disconnect a specified ratio of links with network decomposition. Our method is of high efficiency, which can be easily employed to calculate the Markov criticality in real-life networks. Comprehensive experiments on several synthetic and real-life networks have demonstrated our method’s better performance by comparing it with state-of-the-art baseline approaches.

A global overview of genetically interpretable multimorbidities among common diseases in the UK Biobank

Background Multimorbidities greatly increase the global health burdens, but the landscapes of their genetic risks have not been systematically investigated. Methods We used the hospital inpatient data of 385,335 patients in the UK Biobank to investigate the multimorbid relations among 439 common diseases. Post-GWAS analyses were performed to identify multimorbidity shared genetic risks at the genomic loci, network, as well as overall genetic architecture levels. We conducted network decomposition for the networks of genetically interpretable multimorbidities to detect the hub diseases and the involved molecules and functions in each module. Results In total, 11,285 multimorbidities among 439 common diseases were identified, and 46% of them were genetically interpretable at the loci, network, or overall genetic architecture levels. Multimorbidities affecting the same and different physiological systems displayed different patterns of the shared genetic components, with the former more likely to share loci-level genetic components while the latter more likely to share network-level genetic components. Moreover, both the loci- and network-level genetic components shared by multimorbidities converged on cell immunity, protein metabolism, and gene silencing. Furthermore, we found that the genetically interpretable multimorbidities tend to form network modules, mediated by hub diseases and featuring physiological categories. Finally, we showcased how hub diseases mediating the multimorbidity modules could help provide useful insights for the genetic contributors of multimorbidities. Conclusions Our results provide a systematic resource for understanding the genetic predispositions of multimorbidities and indicate that hub diseases and converged molecules and functions may be the key for treating multimorbidities. We have created an online database that facilitates researchers and physicians to browse, search, or download these multimorbidities (https://multimorbidity.comp-sysbio.org).

Ideal Time-Based Voltage Control using Evolutionary Algorithm in Distributed Generator Centered Networks

The distributed generators (DGs) consists of radial rural distribution networks that makes use of off-voltage tap changing transformers. Ideal tap changer positions for these transformers can be determined using the novel estimation technique proposed in this paper. A branchy low-voltage network is brought down to its equivalent line along with the utilization of spatial network decomposition in this technique. Evolutionary algorithm is used for determining the PV nodes ideal voltage module values in ideal seasonal control plan. A PQ node with 3 DGs incorporated in a radial 40-node network and a PQ node with 10 DGs are incorporated in a radial 33-node network are the distribution networks used for modelling the proposed system.

A global overview of genetically interpretable comorbidities among common diseases in UK Biobank

BackgroundComorbidities greatly increase global health burdens, but the landscapes of their genetic factors have not been systematically investigated.MethodsWe used the hospital inpatient data of 385,335 patients in UK Biobank to investigate the comorbid relations among 439 common diseases. Post-GWAS analyses were performed to identify comorbidity shared genetic risks at the genomic loci, network, as well as overall genetic architecture levels. We conducted network decomposition for interpretable comorbidity networks to detect the hub diseases and the involved molecules in comorbidity modules.Results11,285 comorbidities among 439 common diseases were identified, and 46% of them were genetically interpretable at the loci, network, or overall genetic architecture level. The comorbidities affecting the same and different physiological systems showed different patterns at the shared genetic components, with the former more likely to share loci-level genetic components while the latter more likely to share network-level genetic components. Moreover, both the loci- and network-level genetic components shared by comorbidities mainly converged on cell immunity, protein metabolism, and gene silencing. Furthermore, we found that the genetically interpretable comorbidities tend to form network modules, mediated by hub diseases and featuring physiological categories. Finally, we showcased how hub diseases mediating the comorbidity modules could help provide useful insights into the genetic contributors for comorbiditities.ConclusionsOur results provide a systematic resource for understanding the genetic predispositions of comorbidity, and indicate that hub diseases and converged molecules and functions may be the key for treating comorbidity. We have created an online database to facilitate researchers and physicians to browse, search or download these comorbidities (https://comorbidity.comp-sysbio.org).